Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations11965
Missing cells11094
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 MiB
Average record size in memory924.4 B

Variable types

Categorical19
Text2
Numeric9
Boolean2

Alerts

Floor_No is highly overall correlated with floor_ratioHigh correlation
PTA_level is highly overall correlated with Public_Transport_AccessibilityHigh correlation
Parking_Space is highly overall correlated with Parking_Space_binHigh correlation
Parking_Space_bin is highly overall correlated with Parking_SpaceHigh correlation
Price_in_Lakhs is highly overall correlated with pps_rupeesHigh correlation
Public_Transport_Accessibility is highly overall correlated with PTA_levelHigh correlation
Security is highly overall correlated with Security_binHigh correlation
Security_bin is highly overall correlated with SecurityHigh correlation
Size_in_SqFt is highly overall correlated with pps_rupeesHigh correlation
amenities_count is highly overall correlated with amenity_clubhouse and 4 other fieldsHigh correlation
amenity_clubhouse is highly overall correlated with amenities_countHigh correlation
amenity_garden is highly overall correlated with amenities_countHigh correlation
amenity_gym is highly overall correlated with amenities_countHigh correlation
amenity_playground is highly overall correlated with amenities_countHigh correlation
amenity_pool is highly overall correlated with amenities_countHigh correlation
floor_ratio is highly overall correlated with Floor_No and 2 other fieldsHigh correlation
is_ground is highly overall correlated with floor_ratioHigh correlation
is_top is highly overall correlated with floor_ratioHigh correlation
pps_rupees is highly overall correlated with Price_in_Lakhs and 1 other fieldsHigh correlation
is_ground is highly imbalanced (78.9%) Imbalance
is_top is highly imbalanced (78.1%) Imbalance
Floor_No has 5547 (46.4%) missing values Missing
floor_ratio has 5547 (46.4%) missing values Missing
Floor_No has 399 (3.3%) zeros Zeros
floor_ratio has 399 (3.3%) zeros Zeros

Reproduction

Analysis started2025-08-14 07:22:25.308927
Analysis finished2025-08-14 07:22:43.050379
Duration17.74 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

City
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
ahmedabad
6104 
surat
5861 

Length

Max length9
Median length9
Mean length7.0406185
Min length5

Characters and Unicode

Total characters84241
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowahmedabad
2nd rowahmedabad
3rd rowahmedabad
4th rowahmedabad
5th rowahmedabad

Common Values

ValueCountFrequency (%)
ahmedabad 6104
51.0%
surat 5861
49.0%

Length

2025-08-14T07:22:43.209046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:43.334329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ahmedabad 6104
51.0%
surat 5861
49.0%

Most occurring characters

ValueCountFrequency (%)
a 24173
28.7%
d 12208
14.5%
h 6104
 
7.2%
m 6104
 
7.2%
e 6104
 
7.2%
b 6104
 
7.2%
s 5861
 
7.0%
u 5861
 
7.0%
r 5861
 
7.0%
t 5861
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24173
28.7%
d 12208
14.5%
h 6104
 
7.2%
m 6104
 
7.2%
e 6104
 
7.2%
b 6104
 
7.2%
s 5861
 
7.0%
u 5861
 
7.0%
r 5861
 
7.0%
t 5861
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24173
28.7%
d 12208
14.5%
h 6104
 
7.2%
m 6104
 
7.2%
e 6104
 
7.2%
b 6104
 
7.2%
s 5861
 
7.0%
u 5861
 
7.0%
r 5861
 
7.0%
t 5861
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24173
28.7%
d 12208
14.5%
h 6104
 
7.2%
m 6104
 
7.2%
e 6104
 
7.2%
b 6104
 
7.2%
s 5861
 
7.0%
u 5861
 
7.0%
r 5861
 
7.0%
t 5861
 
7.0%
Distinct500
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-08-14T07:22:43.626885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.784288
Min length10

Characters and Unicode

Total characters140999
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlocality_164
2nd rowlocality_494
3rd rowlocality_330
4th rowlocality_175
5th rowlocality_221
ValueCountFrequency (%)
locality_382 44
 
0.4%
locality_22 37
 
0.3%
locality_240 36
 
0.3%
locality_60 36
 
0.3%
locality_256 35
 
0.3%
locality_418 35
 
0.3%
locality_81 35
 
0.3%
locality_213 35
 
0.3%
locality_266 34
 
0.3%
locality_217 34
 
0.3%
Other values (490) 11604
97.0%
2025-08-14T07:22:44.103298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 23930
17.0%
o 11965
8.5%
c 11965
8.5%
a 11965
8.5%
i 11965
8.5%
t 11965
8.5%
y 11965
8.5%
_ 11965
8.5%
2 4901
 
3.5%
3 4762
 
3.4%
Other values (8) 23651
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 23930
17.0%
o 11965
8.5%
c 11965
8.5%
a 11965
8.5%
i 11965
8.5%
t 11965
8.5%
y 11965
8.5%
_ 11965
8.5%
2 4901
 
3.5%
3 4762
 
3.4%
Other values (8) 23651
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 23930
17.0%
o 11965
8.5%
c 11965
8.5%
a 11965
8.5%
i 11965
8.5%
t 11965
8.5%
y 11965
8.5%
_ 11965
8.5%
2 4901
 
3.5%
3 4762
 
3.4%
Other values (8) 23651
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 23930
17.0%
o 11965
8.5%
c 11965
8.5%
a 11965
8.5%
i 11965
8.5%
t 11965
8.5%
y 11965
8.5%
_ 11965
8.5%
2 4901
 
3.5%
3 4762
 
3.4%
Other values (8) 23651
16.8%

Property_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
independent house
4052 
villa
4035 
apartment
3878 

Length

Max length17
Median length9
Mean length10.360301
Min length5

Characters and Unicode

Total characters123961
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowindependent house
2nd rowapartment
3rd rowindependent house
4th rowapartment
5th rowapartment

Common Values

ValueCountFrequency (%)
independent house 4052
33.9%
villa 4035
33.7%
apartment 3878
32.4%

Length

2025-08-14T07:22:44.235672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:44.312045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
independent 4052
25.3%
house 4052
25.3%
villa 4035
25.2%
apartment 3878
24.2%

Most occurring characters

ValueCountFrequency (%)
e 20086
16.2%
n 16034
12.9%
t 11808
9.5%
a 11791
9.5%
d 8104
 
6.5%
i 8087
 
6.5%
l 8070
 
6.5%
p 7930
 
6.4%
h 4052
 
3.3%
4052
 
3.3%
Other values (6) 23947
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20086
16.2%
n 16034
12.9%
t 11808
9.5%
a 11791
9.5%
d 8104
 
6.5%
i 8087
 
6.5%
l 8070
 
6.5%
p 7930
 
6.4%
h 4052
 
3.3%
4052
 
3.3%
Other values (6) 23947
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20086
16.2%
n 16034
12.9%
t 11808
9.5%
a 11791
9.5%
d 8104
 
6.5%
i 8087
 
6.5%
l 8070
 
6.5%
p 7930
 
6.4%
h 4052
 
3.3%
4052
 
3.3%
Other values (6) 23947
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20086
16.2%
n 16034
12.9%
t 11808
9.5%
a 11791
9.5%
d 8104
 
6.5%
i 8087
 
6.5%
l 8070
 
6.5%
p 7930
 
6.4%
h 4052
 
3.3%
4052
 
3.3%
Other values (6) 23947
19.3%

BHK
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
5
2430 
1
2418 
3
2396 
4
2394 
2
2327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11965
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row5
4th row1
5th row4

Common Values

ValueCountFrequency (%)
5 2430
20.3%
1 2418
20.2%
3 2396
20.0%
4 2394
20.0%
2 2327
19.4%

Length

2025-08-14T07:22:44.420068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:44.515060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 2430
20.3%
1 2418
20.2%
3 2396
20.0%
4 2394
20.0%
2 2327
19.4%

Most occurring characters

ValueCountFrequency (%)
5 2430
20.3%
1 2418
20.2%
3 2396
20.0%
4 2394
20.0%
2 2327
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 2430
20.3%
1 2418
20.2%
3 2396
20.0%
4 2394
20.0%
2 2327
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 2430
20.3%
1 2418
20.2%
3 2396
20.0%
4 2394
20.0%
2 2327
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 2430
20.3%
1 2418
20.2%
3 2396
20.0%
4 2394
20.0%
2 2327
19.4%

Size_in_SqFt
Real number (ℝ)

High correlation 

Distinct4134
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2776.0379
Minimum570
Maximum4952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:44.653186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum570
5-th percentile812.2
Q11670
median2781
Q33885
95-th percentile4740
Maximum4952
Range4382
Interquartile range (IQR)2215

Descriptive statistics

Standard deviation1268.1895
Coefficient of variation (CV)0.45683434
Kurtosis-1.2156178
Mean2776.0379
Median Absolute Deviation (MAD)1107
Skewness-0.0039412232
Sum33215294
Variance1608304.5
MonotonicityNot monotonic
2025-08-14T07:22:44.818620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1188 9
 
0.1%
943 9
 
0.1%
2845 8
 
0.1%
1204 8
 
0.1%
4689 8
 
0.1%
3969 8
 
0.1%
2867 8
 
0.1%
3889 8
 
0.1%
1774 8
 
0.1%
1937 8
 
0.1%
Other values (4124) 11883
99.3%
ValueCountFrequency (%)
570 3
< 0.1%
571 1
 
< 0.1%
572 3
< 0.1%
573 1
 
< 0.1%
574 4
< 0.1%
577 2
 
< 0.1%
578 3
< 0.1%
579 1
 
< 0.1%
580 5
< 0.1%
581 3
< 0.1%
ValueCountFrequency (%)
4952 5
< 0.1%
4951 3
< 0.1%
4950 3
< 0.1%
4949 6
0.1%
4948 3
< 0.1%
4947 2
 
< 0.1%
4946 3
< 0.1%
4945 1
 
< 0.1%
4944 3
< 0.1%
4943 4
< 0.1%

Price_in_Lakhs
Real number (ℝ)

High correlation 

Distinct10601
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.57923
Minimum14.73
Maximum494.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:44.971728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.73
5-th percentile39.004
Q1135.34
median255.51
Q3374.18
95-th percentile469.5
Maximum494.67
Range479.94
Interquartile range (IQR)238.84

Descriptive statistics

Standard deviation138.60735
Coefficient of variation (CV)0.54445663
Kurtosis-1.209462
Mean254.57923
Median Absolute Deviation (MAD)119.38
Skewness-0.0015851415
Sum3046040.4
Variance19211.997
MonotonicityNot monotonic
2025-08-14T07:22:45.124429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321.78 4
 
< 0.1%
53.92 4
 
< 0.1%
39.18 4
 
< 0.1%
269.67 4
 
< 0.1%
456.47 4
 
< 0.1%
158.95 4
 
< 0.1%
461.88 3
 
< 0.1%
313.02 3
 
< 0.1%
155.97 3
 
< 0.1%
281.83 3
 
< 0.1%
Other values (10591) 11929
99.7%
ValueCountFrequency (%)
14.73 1
< 0.1%
14.76 1
< 0.1%
14.96 1
< 0.1%
14.98 1
< 0.1%
14.99 1
< 0.1%
15.01 1
< 0.1%
15.04 1
< 0.1%
15.09 1
< 0.1%
15.1 1
< 0.1%
15.13 1
< 0.1%
ValueCountFrequency (%)
494.67 1
< 0.1%
494.64 1
< 0.1%
494.59 1
< 0.1%
494.58 1
< 0.1%
494.57 1
< 0.1%
494.55 1
< 0.1%
494.53 2
< 0.1%
494.49 2
< 0.1%
494.42 1
< 0.1%
494.4 1
< 0.1%

Furnished_Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
semi-furnished
4021 
unfurnished
4017 
furnished
3927 

Length

Max length14
Median length11
Mean length11.351776
Min length9

Characters and Unicode

Total characters135824
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunfurnished
2nd rowsemi-furnished
3rd rowsemi-furnished
4th rowfurnished
5th rowsemi-furnished

Common Values

ValueCountFrequency (%)
semi-furnished 4021
33.6%
unfurnished 4017
33.6%
furnished 3927
32.8%

Length

2025-08-14T07:22:45.256428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:45.338259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
semi-furnished 4021
33.6%
unfurnished 4017
33.6%
furnished 3927
32.8%

Most occurring characters

ValueCountFrequency (%)
s 15986
11.8%
e 15986
11.8%
i 15986
11.8%
n 15982
11.8%
u 15982
11.8%
h 11965
8.8%
f 11965
8.8%
d 11965
8.8%
r 11965
8.8%
m 4021
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 15986
11.8%
e 15986
11.8%
i 15986
11.8%
n 15982
11.8%
u 15982
11.8%
h 11965
8.8%
f 11965
8.8%
d 11965
8.8%
r 11965
8.8%
m 4021
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 15986
11.8%
e 15986
11.8%
i 15986
11.8%
n 15982
11.8%
u 15982
11.8%
h 11965
8.8%
f 11965
8.8%
d 11965
8.8%
r 11965
8.8%
m 4021
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 15986
11.8%
e 15986
11.8%
i 15986
11.8%
n 15982
11.8%
u 15982
11.8%
h 11965
8.8%
f 11965
8.8%
d 11965
8.8%
r 11965
8.8%
m 4021
 
3.0%

Floor_No
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct31
Distinct (%)0.5%
Missing5547
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean9.9619819
Minimum0
Maximum30
Zeros399
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:45.857511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q315
95-th percentile24
Maximum30
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.4222029
Coefficient of variation (CV)0.74505283
Kurtosis-0.60186077
Mean9.9619819
Median Absolute Deviation (MAD)6
Skewness0.57140685
Sum63936
Variance55.089095
MonotonicityNot monotonic
2025-08-14T07:22:45.985650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 399
 
3.3%
2 396
 
3.3%
1 394
 
3.3%
3 368
 
3.1%
4 350
 
2.9%
5 325
 
2.7%
6 313
 
2.6%
7 307
 
2.6%
8 298
 
2.5%
9 291
 
2.4%
Other values (21) 2977
24.9%
(Missing) 5547
46.4%
ValueCountFrequency (%)
0 399
3.3%
1 394
3.3%
2 396
3.3%
3 368
3.1%
4 350
2.9%
5 325
2.7%
6 313
2.6%
7 307
2.6%
8 298
2.5%
9 291
2.4%
ValueCountFrequency (%)
30 15
 
0.1%
29 29
 
0.2%
28 30
 
0.3%
27 54
0.5%
26 60
0.5%
25 73
0.6%
24 96
0.8%
23 105
0.9%
22 126
1.1%
21 129
1.1%

Total_Floors
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.363477
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:46.115982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6698049
Coefficient of variation (CV)0.56431269
Kurtosis-1.1988513
Mean15.363477
Median Absolute Deviation (MAD)8
Skewness0.019587479
Sum183824
Variance75.165516
MonotonicityNot monotonic
2025-08-14T07:22:46.241903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
15 451
 
3.8%
14 434
 
3.6%
1 427
 
3.6%
25 424
 
3.5%
2 422
 
3.5%
6 416
 
3.5%
13 413
 
3.5%
8 409
 
3.4%
19 408
 
3.4%
16 408
 
3.4%
Other values (20) 7753
64.8%
ValueCountFrequency (%)
1 427
3.6%
2 422
3.5%
3 389
3.3%
4 399
3.3%
5 404
3.4%
6 416
3.5%
7 392
3.3%
8 409
3.4%
9 389
3.3%
10 402
3.4%
ValueCountFrequency (%)
30 381
3.2%
29 407
3.4%
28 385
3.2%
27 394
3.3%
26 408
3.4%
25 424
3.5%
24 374
3.1%
23 374
3.1%
22 381
3.2%
21 369
3.1%

Nearby_Schools
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4738822
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:46.346970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.874726
Coefficient of variation (CV)0.52517133
Kurtosis-1.2271001
Mean5.4738822
Median Absolute Deviation (MAD)2
Skewness0.017590932
Sum65495
Variance8.2640495
MonotonicityNot monotonic
2025-08-14T07:22:46.443288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 1255
10.5%
2 1221
10.2%
7 1220
10.2%
1 1201
10.0%
9 1201
10.0%
3 1197
10.0%
10 1191
10.0%
5 1176
9.8%
6 1161
9.7%
8 1142
9.5%
ValueCountFrequency (%)
1 1201
10.0%
2 1221
10.2%
3 1197
10.0%
4 1255
10.5%
5 1176
9.8%
6 1161
9.7%
7 1220
10.2%
8 1142
9.5%
9 1201
10.0%
10 1191
10.0%
ValueCountFrequency (%)
10 1191
10.0%
9 1201
10.0%
8 1142
9.5%
7 1220
10.2%
6 1161
9.7%
5 1176
9.8%
4 1255
10.5%
3 1197
10.0%
2 1221
10.2%
1 1201
10.0%

Nearby_Hospitals
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4790639
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:46.534233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8802003
Coefficient of variation (CV)0.52567379
Kurtosis-1.2326936
Mean5.4790639
Median Absolute Deviation (MAD)3
Skewness0.021119448
Sum65557
Variance8.2955538
MonotonicityNot monotonic
2025-08-14T07:22:46.631405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 1239
10.4%
3 1230
10.3%
10 1222
10.2%
4 1214
10.1%
7 1183
9.9%
1 1183
9.9%
9 1178
9.8%
5 1177
9.8%
6 1175
9.8%
8 1164
9.7%
ValueCountFrequency (%)
1 1183
9.9%
2 1239
10.4%
3 1230
10.3%
4 1214
10.1%
5 1177
9.8%
6 1175
9.8%
7 1183
9.9%
8 1164
9.7%
9 1178
9.8%
10 1222
10.2%
ValueCountFrequency (%)
10 1222
10.2%
9 1178
9.8%
8 1164
9.7%
7 1183
9.9%
6 1175
9.8%
5 1177
9.8%
4 1214
10.1%
3 1230
10.3%
2 1239
10.4%
1 1183
9.9%

Public_Transport_Accessibility
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
medium
4028 
low
4019 
high
3918 

Length

Max length6
Median length4
Mean length4.3374008
Min length3

Characters and Unicode

Total characters51897
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
medium 4028
33.7%
low 4019
33.6%
high 3918
32.7%

Length

2025-08-14T07:22:46.739914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:46.822691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 4028
33.7%
low 4019
33.6%
high 3918
32.7%

Most occurring characters

ValueCountFrequency (%)
m 8056
15.5%
i 7946
15.3%
h 7836
15.1%
e 4028
7.8%
u 4028
7.8%
d 4028
7.8%
l 4019
7.7%
o 4019
7.7%
w 4019
7.7%
g 3918
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 8056
15.5%
i 7946
15.3%
h 7836
15.1%
e 4028
7.8%
u 4028
7.8%
d 4028
7.8%
l 4019
7.7%
o 4019
7.7%
w 4019
7.7%
g 3918
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 8056
15.5%
i 7946
15.3%
h 7836
15.1%
e 4028
7.8%
u 4028
7.8%
d 4028
7.8%
l 4019
7.7%
o 4019
7.7%
w 4019
7.7%
g 3918
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 8056
15.5%
i 7946
15.3%
h 7836
15.1%
e 4028
7.8%
u 4028
7.8%
d 4028
7.8%
l 4019
7.7%
o 4019
7.7%
w 4019
7.7%
g 3918
7.5%

Parking_Space
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.2 KiB
False
6058 
True
5907 
ValueCountFrequency (%)
False 6058
50.6%
True 5907
49.4%
2025-08-14T07:22:46.908105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Security
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.2 KiB
True
6025 
False
5940 
ValueCountFrequency (%)
True 6025
50.4%
False 5940
49.6%
2025-08-14T07:22:46.964781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct325
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-14T07:22:47.089186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length26
Mean length23.412537
Min length3

Characters and Unicode

Total characters280131
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgym
2nd rowpool, playground, garden, gym
3rd rowpool, gym
4th rowpool
5th rowclubhouse, playground
ValueCountFrequency (%)
playground 7283
20.2%
garden 7266
20.1%
clubhouse 7239
20.0%
pool 7218
20.0%
gym 7120
19.7%
2025-08-14T07:22:47.366104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 28958
 
10.3%
, 24161
 
8.6%
24161
 
8.6%
u 21761
 
7.8%
l 21740
 
7.8%
g 21669
 
7.7%
a 14549
 
5.2%
n 14549
 
5.2%
r 14549
 
5.2%
d 14549
 
5.2%
Other values (8) 79485
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 280131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 28958
 
10.3%
, 24161
 
8.6%
24161
 
8.6%
u 21761
 
7.8%
l 21740
 
7.8%
g 21669
 
7.7%
a 14549
 
5.2%
n 14549
 
5.2%
r 14549
 
5.2%
d 14549
 
5.2%
Other values (8) 79485
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 280131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 28958
 
10.3%
, 24161
 
8.6%
24161
 
8.6%
u 21761
 
7.8%
l 21740
 
7.8%
g 21669
 
7.7%
a 14549
 
5.2%
n 14549
 
5.2%
r 14549
 
5.2%
d 14549
 
5.2%
Other values (8) 79485
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 280131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 28958
 
10.3%
, 24161
 
8.6%
24161
 
8.6%
u 21761
 
7.8%
l 21740
 
7.8%
g 21669
 
7.7%
a 14549
 
5.2%
n 14549
 
5.2%
r 14549
 
5.2%
d 14549
 
5.2%
Other values (8) 79485
28.4%

Facing
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
west
3057 
east
2980 
north
2972 
south
2956 

Length

Max length5
Median length4
Mean length4.495445
Min length4

Characters and Unicode

Total characters53788
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweast
2nd rowsouth
3rd roweast
4th roweast
5th rowwest

Common Values

ValueCountFrequency (%)
west 3057
25.5%
east 2980
24.9%
north 2972
24.8%
south 2956
24.7%

Length

2025-08-14T07:22:47.477197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:47.556500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
west 3057
25.5%
east 2980
24.9%
north 2972
24.8%
south 2956
24.7%

Most occurring characters

ValueCountFrequency (%)
t 11965
22.2%
s 8993
16.7%
e 6037
11.2%
h 5928
11.0%
o 5928
11.0%
w 3057
 
5.7%
a 2980
 
5.5%
n 2972
 
5.5%
r 2972
 
5.5%
u 2956
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 11965
22.2%
s 8993
16.7%
e 6037
11.2%
h 5928
11.0%
o 5928
11.0%
w 3057
 
5.7%
a 2980
 
5.5%
n 2972
 
5.5%
r 2972
 
5.5%
u 2956
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 11965
22.2%
s 8993
16.7%
e 6037
11.2%
h 5928
11.0%
o 5928
11.0%
w 3057
 
5.7%
a 2980
 
5.5%
n 2972
 
5.5%
r 2972
 
5.5%
u 2956
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 11965
22.2%
s 8993
16.7%
e 6037
11.2%
h 5928
11.0%
o 5928
11.0%
w 3057
 
5.7%
a 2980
 
5.5%
n 2972
 
5.5%
r 2972
 
5.5%
u 2956
 
5.5%

Owner_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
owner
4091 
builder
3979 
broker
3895 

Length

Max length7
Median length6
Mean length5.9906394
Min length5

Characters and Unicode

Total characters71678
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowowner
2nd rowowner
3rd rowbroker
4th rowbuilder
5th rowbroker

Common Values

ValueCountFrequency (%)
owner 4091
34.2%
builder 3979
33.3%
broker 3895
32.6%

Length

2025-08-14T07:22:47.661086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:47.738352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
owner 4091
34.2%
builder 3979
33.3%
broker 3895
32.6%

Most occurring characters

ValueCountFrequency (%)
r 15860
22.1%
e 11965
16.7%
o 7986
11.1%
b 7874
11.0%
w 4091
 
5.7%
n 4091
 
5.7%
u 3979
 
5.6%
i 3979
 
5.6%
l 3979
 
5.6%
d 3979
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 15860
22.1%
e 11965
16.7%
o 7986
11.1%
b 7874
11.0%
w 4091
 
5.7%
n 4091
 
5.7%
u 3979
 
5.6%
i 3979
 
5.6%
l 3979
 
5.6%
d 3979
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 15860
22.1%
e 11965
16.7%
o 7986
11.1%
b 7874
11.0%
w 4091
 
5.7%
n 4091
 
5.7%
u 3979
 
5.6%
i 3979
 
5.6%
l 3979
 
5.6%
d 3979
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 15860
22.1%
e 11965
16.7%
o 7986
11.1%
b 7874
11.0%
w 4091
 
5.7%
n 4091
 
5.7%
u 3979
 
5.6%
i 3979
 
5.6%
l 3979
 
5.6%
d 3979
 
5.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
under_construction
6101 
ready_to_move
5864 

Length

Max length18
Median length18
Mean length15.549519
Min length13

Characters and Unicode

Total characters186050
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunder_construction
2nd rowunder_construction
3rd rowready_to_move
4th rowready_to_move
5th rowready_to_move

Common Values

ValueCountFrequency (%)
under_construction 6101
51.0%
ready_to_move 5864
49.0%

Length

2025-08-14T07:22:47.841878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:47.932329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
under_construction 6101
51.0%
ready_to_move 5864
49.0%

Most occurring characters

ValueCountFrequency (%)
o 23930
12.9%
n 18303
9.8%
r 18066
9.7%
t 18066
9.7%
e 17829
9.6%
_ 17829
9.6%
u 12202
6.6%
c 12202
6.6%
d 11965
6.4%
s 6101
 
3.3%
Other values (5) 29557
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 23930
12.9%
n 18303
9.8%
r 18066
9.7%
t 18066
9.7%
e 17829
9.6%
_ 17829
9.6%
u 12202
6.6%
c 12202
6.6%
d 11965
6.4%
s 6101
 
3.3%
Other values (5) 29557
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 23930
12.9%
n 18303
9.8%
r 18066
9.7%
t 18066
9.7%
e 17829
9.6%
_ 17829
9.6%
u 12202
6.6%
c 12202
6.6%
d 11965
6.4%
s 6101
 
3.3%
Other values (5) 29557
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 23930
12.9%
n 18303
9.8%
r 18066
9.7%
t 18066
9.7%
e 17829
9.6%
_ 17829
9.6%
u 12202
6.6%
c 12202
6.6%
d 11965
6.4%
s 6101
 
3.3%
Other values (5) 29557
15.9%

PTA_level
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
4028 
0
4019 
2
3918 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11965
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 4028
33.7%
0 4019
33.6%
2 3918
32.7%

Length

2025-08-14T07:22:48.026211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:48.102895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4028
33.7%
0 4019
33.6%
2 3918
32.7%

Most occurring characters

ValueCountFrequency (%)
1 4028
33.7%
0 4019
33.6%
2 3918
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4028
33.7%
0 4019
33.6%
2 3918
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4028
33.7%
0 4019
33.6%
2 3918
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4028
33.7%
0 4019
33.6%
2 3918
32.7%

Parking_Space_bin
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
6058 
1
5907 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 6058
50.6%
1 5907
49.4%

Length

2025-08-14T07:22:48.199275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:48.270990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6058
50.6%
1 5907
49.4%

Most occurring characters

ValueCountFrequency (%)
0 6058
50.6%
1 5907
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6058
50.6%
1 5907
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6058
50.6%
1 5907
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6058
50.6%
1 5907
49.4%

Security_bin
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
6025 
0
5940 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6025
50.4%
0 5940
49.6%

Length

2025-08-14T07:22:48.362505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:48.430115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6025
50.4%
0 5940
49.6%

Most occurring characters

ValueCountFrequency (%)
1 6025
50.4%
0 5940
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6025
50.4%
0 5940
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6025
50.4%
0 5940
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6025
50.4%
0 5940
49.6%

pps_rupees
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12339.49
Minimum0
Maximum66000
Zeros65
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:48.530412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q15000
median9000
Q316000
95-th percentile37000
Maximum66000
Range66000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation11184.283
Coefficient of variation (CV)0.90638127
Kurtosis4.1538553
Mean12339.49
Median Absolute Deviation (MAD)5000
Skewness1.9123057
Sum1.47642 × 108
Variance1.2508818 × 108
MonotonicityNot monotonic
2025-08-14T07:22:48.676093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 715
 
6.0%
10000 709
 
5.9%
9000 701
 
5.9%
2000 692
 
5.8%
4000 684
 
5.7%
7000 677
 
5.7%
8000 675
 
5.6%
3000 671
 
5.6%
6000 668
 
5.6%
11000 631
 
5.3%
Other values (57) 5142
43.0%
ValueCountFrequency (%)
0 65
 
0.5%
1000 604
5.0%
2000 692
5.8%
3000 671
5.6%
4000 684
5.7%
5000 715
6.0%
6000 668
5.6%
7000 677
5.7%
8000 675
5.6%
9000 701
5.9%
ValueCountFrequency (%)
66000 10
0.1%
65000 11
0.1%
64000 6
 
0.1%
63000 10
0.1%
62000 10
0.1%
61000 6
 
0.1%
60000 6
 
0.1%
59000 14
0.1%
58000 16
0.1%
57000 8
0.1%

age_years
Real number (ℝ)

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.511659
Minimum2
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:48.814513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median19
Q327
95-th percentile34
Maximum35
Range33
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.7274528
Coefficient of variation (CV)0.5254771
Kurtosis-1.1904787
Mean18.511659
Median Absolute Deviation (MAD)8
Skewness-0.0047020932
Sum221492
Variance94.623339
MonotonicityNot monotonic
2025-08-14T07:22:48.936446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
15 389
 
3.3%
11 381
 
3.2%
12 375
 
3.1%
25 373
 
3.1%
24 371
 
3.1%
30 369
 
3.1%
21 369
 
3.1%
20 361
 
3.0%
8 360
 
3.0%
10 360
 
3.0%
Other values (24) 8257
69.0%
ValueCountFrequency (%)
2 350
2.9%
3 347
2.9%
4 331
2.8%
5 334
2.8%
6 333
2.8%
7 342
2.9%
8 360
3.0%
9 342
2.9%
10 360
3.0%
11 381
3.2%
ValueCountFrequency (%)
35 317
2.6%
34 332
2.8%
33 353
3.0%
32 356
3.0%
31 358
3.0%
30 369
3.1%
29 348
2.9%
28 355
3.0%
27 357
3.0%
26 342
2.9%

floor_ratio
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct279
Distinct (%)4.3%
Missing5547
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean0.50142526
Minimum0
Maximum1
Zeros399
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size445.0 KiB
2025-08-14T07:22:49.090426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.23076923
median0.5
Q30.76923077
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.53846154

Descriptive statistics

Standard deviation0.30950555
Coefficient of variation (CV)0.6172516
Kurtosis-1.2071518
Mean0.50142526
Median Absolute Deviation (MAD)0.26923077
Skewness-0.01984892
Sum3218.1473
Variance0.095793683
MonotonicityNot monotonic
2025-08-14T07:22:49.230863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 418
 
3.5%
0 399
 
3.3%
0.5 209
 
1.7%
0.3333333333 127
 
1.1%
0.6666666667 118
 
1.0%
0.75 94
 
0.8%
0.6 88
 
0.7%
0.25 84
 
0.7%
0.8 81
 
0.7%
0.2 77
 
0.6%
Other values (269) 4723
39.5%
(Missing) 5547
46.4%
ValueCountFrequency (%)
0 399
3.3%
0.03333333333 23
 
0.2%
0.03448275862 9
 
0.1%
0.03571428571 11
 
0.1%
0.03703703704 13
 
0.1%
0.03846153846 17
 
0.1%
0.04 14
 
0.1%
0.04166666667 12
 
0.1%
0.04347826087 16
 
0.1%
0.04545454545 11
 
0.1%
ValueCountFrequency (%)
1 418
3.5%
0.9666666667 11
 
0.1%
0.9655172414 9
 
0.1%
0.9642857143 12
 
0.1%
0.962962963 11
 
0.1%
0.9615384615 7
 
0.1%
0.96 11
 
0.1%
0.9583333333 12
 
0.1%
0.9565217391 15
 
0.1%
0.9545454545 16
 
0.1%

is_ground
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0.0
11566 
1.0
 
399

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11566
96.7%
1.0 399
 
3.3%

Length

2025-08-14T07:22:49.353872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:49.425074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11566
96.7%
1.0 399
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 23531
65.6%
. 11965
33.3%
1 399
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23531
65.6%
. 11965
33.3%
1 399
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23531
65.6%
. 11965
33.3%
1 399
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23531
65.6%
. 11965
33.3%
1 399
 
1.1%

is_top
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0.0
11547 
1.0
 
418

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11547
96.5%
1.0 418
 
3.5%

Length

2025-08-14T07:22:49.507918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:49.576354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11547
96.5%
1.0 418
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 23512
65.5%
. 11965
33.3%
1 418
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23512
65.5%
. 11965
33.3%
1 418
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23512
65.5%
. 11965
33.3%
1 418
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23512
65.5%
. 11965
33.3%
1 418
 
1.2%

amenity_pool
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1.0
7218 
0.0
4747 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 7218
60.3%
0.0 4747
39.7%

Length

2025-08-14T07:22:49.652243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:49.716688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7218
60.3%
0.0 4747
39.7%

Most occurring characters

ValueCountFrequency (%)
0 16712
46.6%
. 11965
33.3%
1 7218
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16712
46.6%
. 11965
33.3%
1 7218
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16712
46.6%
. 11965
33.3%
1 7218
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16712
46.6%
. 11965
33.3%
1 7218
20.1%

amenity_garden
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1.0
7266 
0.0
4699 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 7266
60.7%
0.0 4699
39.3%

Length

2025-08-14T07:22:49.800525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:49.873647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7266
60.7%
0.0 4699
39.3%

Most occurring characters

ValueCountFrequency (%)
0 16664
46.4%
. 11965
33.3%
1 7266
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16664
46.4%
. 11965
33.3%
1 7266
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16664
46.4%
. 11965
33.3%
1 7266
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16664
46.4%
. 11965
33.3%
1 7266
20.2%

amenity_gym
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1.0
7120 
0.0
4845 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 7120
59.5%
0.0 4845
40.5%

Length

2025-08-14T07:22:49.962523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:50.053589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7120
59.5%
0.0 4845
40.5%

Most occurring characters

ValueCountFrequency (%)
0 16810
46.8%
. 11965
33.3%
1 7120
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16810
46.8%
. 11965
33.3%
1 7120
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16810
46.8%
. 11965
33.3%
1 7120
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16810
46.8%
. 11965
33.3%
1 7120
19.8%

amenity_playground
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1.0
7283 
0.0
4682 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7283
60.9%
0.0 4682
39.1%

Length

2025-08-14T07:22:50.143860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:50.216104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7283
60.9%
0.0 4682
39.1%

Most occurring characters

ValueCountFrequency (%)
0 16647
46.4%
. 11965
33.3%
1 7283
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16647
46.4%
. 11965
33.3%
1 7283
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16647
46.4%
. 11965
33.3%
1 7283
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16647
46.4%
. 11965
33.3%
1 7283
20.3%

amenity_clubhouse
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1.0
7239 
0.0
4726 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7239
60.5%
0.0 4726
39.5%

Length

2025-08-14T07:22:50.309495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:50.381665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7239
60.5%
0.0 4726
39.5%

Most occurring characters

ValueCountFrequency (%)
0 16691
46.5%
. 11965
33.3%
1 7239
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16691
46.5%
. 11965
33.3%
1 7239
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16691
46.5%
. 11965
33.3%
1 7239
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16691
46.5%
. 11965
33.3%
1 7239
20.2%

amenities_count
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
5.0
2484 
4.0
2401 
2.0
2388 
1.0
2375 
3.0
2317 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35895
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
5.0 2484
20.8%
4.0 2401
20.1%
2.0 2388
20.0%
1.0 2375
19.8%
3.0 2317
19.4%

Length

2025-08-14T07:22:50.466712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T07:22:50.549487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 2484
20.8%
4.0 2401
20.1%
2.0 2388
20.0%
1.0 2375
19.8%
3.0 2317
19.4%

Most occurring characters

ValueCountFrequency (%)
. 11965
33.3%
0 11965
33.3%
5 2484
 
6.9%
4 2401
 
6.7%
2 2388
 
6.7%
1 2375
 
6.6%
3 2317
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 11965
33.3%
0 11965
33.3%
5 2484
 
6.9%
4 2401
 
6.7%
2 2388
 
6.7%
1 2375
 
6.6%
3 2317
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 11965
33.3%
0 11965
33.3%
5 2484
 
6.9%
4 2401
 
6.7%
2 2388
 
6.7%
1 2375
 
6.6%
3 2317
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 11965
33.3%
0 11965
33.3%
5 2484
 
6.9%
4 2401
 
6.7%
2 2388
 
6.7%
1 2375
 
6.6%
3 2317
 
6.5%

Interactions

2025-08-14T07:22:40.359186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:30.978973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.991248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.011945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:34.802700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.848620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.856473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.939594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.320783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.484053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.093711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.116484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.138919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:34.916916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.954318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.963295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:38.054198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.455987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.653145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.201062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.219815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.260799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.029825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.063279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.082312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:38.178908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.560153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.824367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.320128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.338069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.379700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.140049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.177849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.196459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:38.304531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.671153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.994618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.441570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.447485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.512345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.270064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.311580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.328383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:38.444720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.794037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:41.169596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.552260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.548901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.627770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.377926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.418126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.443629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:38.554159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.903458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:41.328693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.664316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.655119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:33.742110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.495109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.529227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.559559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:38.675256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.015015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:41.511615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.772501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.769917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:34.589650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.620018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.642441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.698896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.092468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.138088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:41.694632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:31.879743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:32.883113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:34.698763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:35.735801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:36.746034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:37.819794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:39.208026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T07:22:40.245235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-14T07:22:50.690761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Availability_StatusBHKCityFacingFloor_NoFurnished_StatusNearby_HospitalsNearby_SchoolsOwner_TypePTA_levelParking_SpaceParking_Space_binPrice_in_LakhsProperty_TypePublic_Transport_AccessibilitySecuritySecurity_binSize_in_SqFtTotal_Floorsage_yearsamenities_countamenity_clubhouseamenity_gardenamenity_gymamenity_playgroundamenity_poolfloor_ratiois_groundis_toppps_rupees
Availability_Status1.0000.0000.0200.0000.0000.0000.0000.0310.0180.0000.0030.0030.0220.0000.0000.0150.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0110.000
BHK0.0001.0000.0150.0120.0000.0050.0090.0120.0080.0100.0000.0000.0000.0160.0100.0190.0190.0000.0110.0000.0000.0000.0230.0000.0000.0030.0000.0000.0180.000
City0.0200.0151.0000.0000.0000.0000.0150.0290.0000.0140.0000.0000.0000.0070.0140.0000.0000.0190.0200.0140.0050.0000.0000.0000.0000.0000.0000.0000.0000.019
Facing0.0000.0120.0001.0000.0000.0000.0000.0000.0000.0000.0080.0080.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0140.0000.0000.000
Floor_No0.0000.0000.0000.0001.0000.010-0.006-0.0020.0000.0100.0000.000-0.0190.0000.0100.0280.028-0.0250.465-0.0160.0180.0250.0260.0000.0000.0000.8200.4530.307-0.002
Furnished_Status0.0000.0050.0000.0000.0101.0000.0000.0050.0000.0130.0000.0000.0000.0000.0130.0000.0000.0000.0200.0000.0130.0090.0000.0000.0000.0020.0030.0000.0000.014
Nearby_Hospitals0.0000.0090.0150.000-0.0060.0001.000-0.0060.0000.0170.0000.000-0.0070.0000.0170.0000.0000.0000.0060.0030.0150.0140.0000.0070.0000.000-0.0130.0210.000-0.007
Nearby_Schools0.0310.0120.0290.000-0.0020.005-0.0061.0000.0110.0210.0000.0000.0010.0040.0210.0000.0000.006-0.0030.0040.0000.0080.0000.0000.0000.0170.0030.0000.000-0.003
Owner_Type0.0180.0080.0000.0000.0000.0000.0000.0111.0000.0200.0000.0000.0000.0160.0200.0000.0000.0160.0000.0000.0000.0190.0000.0000.0120.0000.0310.0000.0000.000
PTA_level0.0000.0100.0140.0000.0100.0130.0170.0210.0201.0000.0090.0090.0000.0001.0000.0000.0000.0150.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Parking_Space0.0030.0000.0000.0080.0000.0000.0000.0000.0000.0091.0001.0000.0170.0000.0090.0000.0000.0040.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.029
Parking_Space_bin0.0030.0000.0000.0080.0000.0000.0000.0000.0000.0091.0001.0000.0170.0000.0090.0000.0000.0040.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.029
Price_in_Lakhs0.0220.0000.0000.000-0.0190.000-0.0070.0010.0000.0000.0170.0171.0000.0000.0000.0170.0170.019-0.013-0.0030.0000.0000.0100.0000.0270.0000.0000.0080.0000.748
Property_Type0.0000.0160.0070.0130.0000.0000.0000.0040.0160.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0140.0000.0330.0000.0160.0000.0000.0000.0000.000
Public_Transport_Accessibility0.0000.0100.0140.0000.0100.0130.0170.0210.0201.0000.0090.0090.0000.0001.0000.0000.0000.0150.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Security0.0150.0190.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0170.0000.0001.0001.0000.0000.0000.0270.0160.0000.0220.0160.0000.0000.0000.0060.0040.019
Security_bin0.0150.0190.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0170.0000.0001.0001.0000.0000.0000.0270.0160.0000.0220.0160.0000.0000.0000.0060.0040.019
Size_in_SqFt0.0000.0000.0190.000-0.0250.0000.0000.0060.0160.0150.0040.0040.0190.0000.0150.0000.0001.000-0.0100.0120.0100.0230.0000.0000.0000.010-0.0160.0140.030-0.587
Total_Floors0.0000.0110.0200.0000.4650.0200.006-0.0030.0000.0000.0000.000-0.0130.0000.0000.0000.000-0.0101.000-0.0130.0090.0110.0310.0080.0000.017-0.0040.0000.000-0.006
age_years0.0000.0000.0140.000-0.0160.0000.0030.0040.0000.0100.0000.000-0.0030.0000.0100.0270.0270.012-0.0131.0000.0000.0000.0190.0000.0000.008-0.0020.0070.000-0.010
amenities_count0.0000.0000.0050.0000.0180.0130.0150.0000.0000.0000.0000.0000.0000.0140.0000.0160.0160.0100.0090.0001.0000.5820.5700.5890.5810.5860.0100.0000.0050.000
amenity_clubhouse0.0000.0000.0000.0000.0250.0090.0140.0080.0190.0000.0000.0000.0000.0000.0000.0000.0000.0230.0110.0000.5821.0000.1540.1800.1730.1860.0000.0000.0000.018
amenity_garden0.0000.0230.0000.0070.0260.0000.0000.0000.0000.0000.0140.0140.0100.0330.0000.0220.0220.0000.0310.0190.5700.1541.0000.1770.1730.1540.0100.0000.0000.000
amenity_gym0.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0160.0160.0000.0080.0000.5890.1800.1771.0000.1670.1840.0290.0000.0210.029
amenity_playground0.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0270.0160.0000.0000.0000.0000.0000.0000.5810.1730.1730.1671.0000.1780.0000.0000.0000.000
amenity_pool0.0000.0030.0000.0000.0000.0020.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0170.0080.5860.1860.1540.1840.1781.0000.0000.0000.0000.023
floor_ratio0.0000.0000.0000.0140.8200.003-0.0130.0030.0310.0000.0000.0000.0000.0000.0000.0000.000-0.016-0.004-0.0020.0100.0000.0100.0290.0000.0001.0000.6620.7070.007
is_ground0.0080.0000.0000.0000.4530.0000.0210.0000.0000.0000.0000.0000.0080.0000.0000.0060.0060.0140.0000.0070.0000.0000.0000.0000.0000.0000.6621.0000.0330.000
is_top0.0110.0180.0000.0000.3070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0040.0300.0000.0000.0050.0000.0000.0210.0000.0000.7070.0331.0000.000
pps_rupees0.0000.0000.0190.000-0.0020.014-0.007-0.0030.0000.0000.0290.0290.7480.0000.0000.0190.019-0.587-0.006-0.0100.0000.0180.0000.0290.0000.0230.0070.0000.0001.000

Missing values

2025-08-14T07:22:42.040523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-14T07:22:42.502707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-14T07:22:42.907177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CityLocalityProperty_TypeBHKSize_in_SqFtPrice_in_LakhsFurnished_StatusFloor_NoTotal_FloorsNearby_SchoolsNearby_HospitalsPublic_Transport_AccessibilityParking_SpaceSecurityAmenitiesFacingOwner_TypeAvailability_StatusPTA_levelParking_Space_binSecurity_binpps_rupeesage_yearsfloor_ratiois_groundis_topamenity_poolamenity_gardenamenity_gymamenity_playgroundamenity_clubhouseamenities_count
0ahmedabadlocality_164independent house2479377.02unfurnished2.05.017highnonogymeastownerunder_construction2002000.012.00.4000000.00.00.00.01.00.00.01.0
1ahmedabadlocality_494apartment44003311.90semi-furnishedNaN11.012lownonopool, playground, garden, gymsouthownerunder_construction0008000.015.0NaN0.00.01.01.01.01.00.04.0
2ahmedabadlocality_330independent house5220333.56semi-furnishedNaN12.0110lowyesyespool, gymeastbrokerready_to_move0112000.015.0NaN0.00.01.00.01.00.00.02.0
3ahmedabadlocality_175apartment12277305.84furnishedNaN13.0110lowyesyespooleastbuilderready_to_move01113000.031.0NaN0.00.01.00.00.00.00.01.0
4ahmedabadlocality_221apartment41743108.43semi-furnishedNaN3.026lownonoclubhouse, playgroundwestbrokerready_to_move0006000.015.0NaN0.00.00.00.00.01.01.02.0
5ahmedabadlocality_490apartment13437266.57furnishedNaN20.038highnonoclubhouse, playground, garden, poolwestownerready_to_move2008000.09.0NaN0.00.01.01.00.01.01.04.0
6ahmedabadlocality_373independent house21762399.04semi-furnishedNaN2.037highyesyesgarden, playgroundwestbrokerunder_construction21123000.02.0NaN0.00.00.01.00.01.00.02.0
8ahmedabadlocality_243apartment54915159.78furnished6.019.0510highyesnogarden, gym, playground, poolnorthbuilderunder_construction2103000.020.00.3157890.00.01.01.01.01.00.04.0
9ahmedabadlocality_373independent house42506406.86furnishedNaN14.038mediumyesnopool, clubhouse, gym, playgroundsouthbrokerunder_construction11016000.015.0NaN0.00.01.00.01.01.01.04.0
10ahmedabadlocality_355villa42825194.45furnishedNaN12.053highnoyesgardennorthbrokerready_to_move2017000.018.0NaN0.00.00.01.00.00.00.01.0
CityLocalityProperty_TypeBHKSize_in_SqFtPrice_in_LakhsFurnished_StatusFloor_NoTotal_FloorsNearby_SchoolsNearby_HospitalsPublic_Transport_AccessibilityParking_SpaceSecurityAmenitiesFacingOwner_TypeAvailability_StatusPTA_levelParking_Space_binSecurity_binpps_rupeesage_yearsfloor_ratiois_groundis_topamenity_poolamenity_gardenamenity_gymamenity_playgroundamenity_clubhouseamenities_count
12568suratlocality_195villa3273467.28furnished7.017.0109lownonoplayground, clubhouseeastbuilderunder_construction0002000.034.00.4117650.00.00.00.00.01.01.02.0
12569suratlocality_288apartment22082408.52unfurnished8.028.082mediumnoyesplayground, gardeneastbuilderunder_construction10120000.013.00.2857140.00.00.01.00.01.00.02.0
12570suratlocality_34apartment2676216.10unfurnishedNaN15.0101lowyesnopool, clubhouseeastbuilderunder_construction01032000.026.0NaN0.00.01.00.00.00.01.02.0
12571suratlocality_263independent house52289473.24semi-furnishedNaN5.036mediumnoyesgarden, gymeastbrokerunder_construction10121000.08.0NaN0.00.00.01.01.00.00.02.0
12572suratlocality_55independent house23033488.24furnished18.025.067mediumyesyespool, garden, clubhousesouthbuilderready_to_move11116000.028.00.7200000.00.01.01.00.00.01.03.0
12573suratlocality_129apartment4447629.35furnishedNaN9.034lowyesyesplayground, pool, gymeastbuilderready_to_move0111000.028.0NaN0.00.01.00.01.01.00.03.0
12574suratlocality_111independent house52947410.00furnishedNaN24.053mediumyesnogarden, gym, poolsouthbrokerunder_construction11014000.018.0NaN0.00.01.01.01.00.00.03.0
12575suratlocality_418independent house33757470.63semi-furnished11.023.062highyesyesgymsouthbuilderunder_construction21113000.07.00.4782610.00.00.00.01.00.00.01.0
12576suratlocality_169villa12318234.83furnishedNaN16.096highyesyesgymeastownerunder_construction21110000.028.0NaN0.00.00.00.01.00.00.01.0
12577suratlocality_200villa23491146.89unfurnishedNaN4.059lownoyesgym, gardeneastownerunder_construction0014000.021.0NaN0.00.00.01.01.00.00.02.0